Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and ne...
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Wireless Mesh Networks (WMNs) are increasingly being used in a variety of applications. To fully utilize the network resources of WMNs, it is critical to design a topology that provides the best client coverage and network connectivity. This issue is solved by determining the best solution for the mesh router placement problem in WMN (MRP-WMN). Because the MRP-WMN is known to be NP-hard, it is typically solved using approximation algorithms. This is also why we are conducting this work. We present an efficient method for solving the MRP-WMN using the multi-verse optimizer algorithm (MVO). A new objective function for the MRP-WMN is also proposed, which takes into account two important performance metrics, connected client ratio and connected router ratio. Experiment results show that when the MVO algorithm is applied to the MRP-WMN problem, the connected client ratio increases by 15.1%, 11.5%, and 5.9% on average, and the path loss reduces by 1.3, 0.9, and 0.6 dB when compared to the Genetic algorithm (GA), Particle Swarm Optimization (PSO), and Whale Optimization algorithm (WOA), respectively.
This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind ...
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This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind of the most recent powerful nature-inspired meta-heuristic algorithms, where it has been successfully implemented and utilized in several optimization problems in a variety of several fields, which are covered in this context, such as benchmark test functions, machine learning applications, engineering applications, network applications, parameters control, and other applications of MOA. This paper covers all the available publications that have been used MOA in its application, which are published in the literature including the variants of MOA such as binary, modifications, hybridizations, chaotic, and multi-objective. Followed by its applications, the assessment and evaluation, and finally the conclusions, which interested in the current works on the optimization algorithm, recommend potential future research directions.
Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in thes...
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Hexamethylenetetramine (HA) is widely used as a raw material in the medical, chemical, industrial, and military industries, and the fast and quantitative analysis of HA is important for manufacturing processes in these fields. Owing to its efficiency, low cost, nondestructive testing, and convenience, near infrared (NIR) spectroscopy is a powerful technique for quantitatively analyzing the HA concentration in HA-acetic acid (HAc) solutions, demonstrating application potential in the production of hexogen and octogen. A series of preprocessing algorithms and variable selection methods were studied to improve the detection accuracy of the NIR spectroscopic calibration. Forty-six different combinations of standard normal variation (SNV), multiplicative signal correction (MSC), first derivative (1stDer), second derivative (2ndDer), and discrete wavelet transform (DWT) were screened. The effects of four variable selection methods (successive projection algorithm (SPA), uninformed variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and multiverse optimization (MVO)) were compared. Finally, a model (SPXY-SNV-1stDer-DWT-MVO-RF) was developed by combining sample set portioning based on the joint x-y distance (SPXY) algorithm with the random forest (RF) calibration model, and MVO was combined with the NIR technique for the first time. The model achieved a coefficient of determination for the calibration set (R2), root mean square error of the calibration set (RMSEC), coefficient of determination for the prediction set (r2), and root mean square error of the prediction set (RMSEP) of 1.000, 0.04%, 0.999, and 0.05%, respectively. This study demonstrated the novelty and feasibility of HA quantitative detection by NIR spectroscopy and provided valuable insights for optimizing quantitative analysis models by optimizing algorithms, indicating the great application potential of NIR spectroscopy in related fields.
This paper presents a hybrid machine-learning method based on oil-immersed power transformer fault diagnosis Probability Neural Network (PNN) optimized via a multi-verseoptimizer (MVO) algorithm. PNN is a radial basi...
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This paper presents a hybrid machine-learning method based on oil-immersed power transformer fault diagnosis Probability Neural Network (PNN) optimized via a multi-verseoptimizer (MVO) algorithm. PNN is a radial basis function prefeedback neural network based on Bayesian decision theory. It has strong fault tolerance and has significant advantages in pattern classification. However, the performance of PNN is greatly affected by the hidden-layer unit-smoothing factor, and the classification result is affected. MVO is a metaheuristic algorithm with strong global convergence. Therefore, the smoothing factor of MVO-optimized PNN (MVO-PNN) can effectively improve the fault diagnosis ability. Recent studies have demonstrated the MVO algorithm. We utilize an experiment about the oil data in the power transformer in Jiangxi Province, China. The results show that MVO-PNN can significantly improve the accuracy of power transformer fault classification and is more efficient than the Cuckoo search algorithm, Bat algorithm, Genetic algorithm optimization, and other algorithms capabilities in some cases. (c) 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
This paper proposes a maiden application of the fractional-order proportional-integral-derivative plus the second-order derivative controller called FOPIDD2 (PI lambda D mu D2) to achieve better transient response at ...
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This paper proposes a maiden application of the fractional-order proportional-integral-derivative plus the second-order derivative controller called FOPIDD2 (PI lambda D mu D2) to achieve better transient response at a terminal voltage of automatic voltage regulator (AVR). multi-verseoptimizer (MVO) algorithm is used for tuning six optimization parameters of controller. MVO is a powerful optimization algorithm with great convergence speed and strong search mechanisms for an expanded number of decisions. The proposed FOPIDD2 controller is compared with proportional-integral-derivative (PID), PID plus second-order derivative (PIDD2), fractional order PID (FOPID) and filtered FOPID controllers obtained using different algorithms in both time domain and frequency domain. According to the results, FOPIDD2 controller shows superior performance than compared studies in terms of transient response of terminal voltage of AVR. Additionally, frequency domain analysis shows that FOPIDD2 controller presents satisfactory results. Finally, the system is subjected to a robustness test with changes in time constants and gain constants values. AVR system with FOPIDD2 controller performs better in perturbed system parameters than compared controllers.
Photovoltaic power generation is gradually developing into a massive power industry with the maturity of renewable energy power generation technologies. Photovoltaic power generation is greatly affected by external fa...
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Photovoltaic power generation is gradually developing into a massive power industry with the maturity of renewable energy power generation technologies. Photovoltaic power generation is greatly affected by external factors and the output power is characterized by randomness and indirectness, which poses a great challenge to photovoltaic grid-connection. A hybrid improved multi-verse optimizer algorithm (HIMVO) is proposed to optimize the support vector machine for photovoltaic output prediction. HIMVO algorithm introduces chaotic sequences to initialize the population, which significantly enhances the convergence rate of the algorithm compared with the multi-universeoptimizeralgorithm. This study applied particle swarm optimization algorithm, dragonfly algorithm, multi-universeoptimizeralgorithm and HIMVO to testify the availability of the hybrid improved multi-verseoptimizer support vector machine model (HIMVO-SVM). The results indicate that HIMVO algorithm has better optimization ability and stability. The four models, HIMVO-SVM, multi-verseoptimizer support vector machine, particle swarm optimization support vector machine, back propagation and radical basis function neural network are used to predict output in three different weather types. The results indicate that the model has higher prediction accuracy and stability. The mean square error value of the HIMVO-SVM model decreases by at least 0.0026, 0.0030 and 0.0012, and the mean absolute percentage error value decreases by at least 3.6768%, 1.9772% and 2.7165%, respectively. The proposed method is beneficial to the prediction of output power and conduces to the economic dispatch of the grid and the maintenance of the stability of the power system. (C) 2019 Elsevier Ltd. All rights reserved.
In this paper, a new hybrid technique involves a fuzzy expert system and multi-verseoptimizer approach is proposed for optimal locations placement and sizing of capacitors in a radial distribution system. First, the ...
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ISBN:
(纸本)9781538625774
In this paper, a new hybrid technique involves a fuzzy expert system and multi-verseoptimizer approach is proposed for optimal locations placement and sizing of capacitors in a radial distribution system. First, the locations of the shunt capacitors placement are specified using the fuzzy expert system. Then, the multi-verseoptimizer approach is employed to find the size of these capacitors. The objective function is modeled to reduce the system power loss and consequently to increase the net saving as well as voltage profile improvement. The suggested technique is tested on an IEEE 33-nodes test system level of a smart grid. The obtained results via the proposed technique are compared with another algorithm such as Particle Swarm Optimization to emphasize the benefits of this technique. Moreover, the results are introduced to verify its' effectiveness in minimizing the losses, enhancing the voltage profile, and maximizing the net saving.
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